We found that scaling Mamba to substantial sizes isn’t trivial and causes unstable training. We had to make some adjustments to the Mamba layers to stabilize them, as can be seen in the graph below (training loss before & after the change) 6/6
RESEARCH
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Jamba combines Transformer, Mamba, MoE for efficient scaling
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Combining Transformer, Mamba & MoE allows flexibility in balancing low memory usage, high throughput, and high quality. Jamba’s KV cache – which becomes a limiting factor when scaling context in pure Transformers – is 8x smaller compared to a pure Transformer. 5/6
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Jamba’s Impressive Long-Context Performance with Minimal Attention Layers
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Jamba was trained to handle contexts of up to 256K. Jamba has excellent performance in the needle-in-a-haystack evaluation, which is especially interesting given its use of only 4 attention layers. It also outperforms Mixtral on most long-context benchmarks. 4/6
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Mamba Models Struggle with In-Context Learning Compared to Attention
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We noticed that pure Mamba models struggle to develop in-context learning capabilities. E.g., they performed substantially worse than the pure attention model in 3 common benchmarks while the attention–Mamba exhibits similar results to just Transformers. 3/6
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Jamba Hybrid Model Outperforms Pure Attention and Mamba Architectures
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We see that the hybrid Jamba model outperforms both pure attention and pure Mamba models. The ratio of attention-to-Mamba layers of either 1:3 or 1:7 performs comparably, but given that a 1:7 ratio is more compute-efficient, we opt for it in our model. 2/6
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Jamba Whitepaper Released: Hybrid SSM-Transformer Architecture Details
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Jamba whitepaper is out!
The whitepaper details our in-depth ablations on this novel hybrid SSM-Transformer architecture, and how we chose to interleave Mamba, Transformer and MoE. https://
arxiv.org/abs/2403.19887 Here are some highlights from the paper 1/6 -
OpenAI Releases AGI Later Today
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You heard it here first folks, @OpenAI is releasing AGI later today!
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10 Emerging Innovations That Could Redefine IT
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10 emerging #Innovations that could redefine #IT
by @peterwayner @CIOonline Read more: https://
buff.ly/3JRhiKW #AI #BigData #MachineLearning #ArtificialIntelligence #Cloud #ML #Blockchain cc: @terenceleungsf @yvesmulkers @pascal_bornet -
Fine-tuning T5 Model for Stack Overflow Question Tags Generation
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🎥 New Video Alert 🎥https://t.co/JRiqDVpjdo
— Satya Mallick (@LearnOpenCV) 1 avril 2024
Check out our latest video, in which we explore the power of fine-tuning the T5 (Text-To-Text Transfer Transformer) model. We discuss the process of tailoring the T5 model for generating Stack Overflow question tags, showcasing its… pic.twitter.com/RF1395rJxjNew Alert https://
youtube.com/watch?v=u6HYYi
z2MyE
… Check out our latest video, in which we explore the power of fine-tuning the T5 (Text-To-Text Transfer Transformer) model. We discuss the process of tailoring the T5 model for generating Stack Overflow question tags, showcasing its